Vehicle pollution is a pervasive aspect of anthropogenic change across rural and urban habitats. The most common emissions are carbon- or nitrogen-based pollutants that may impact diverse interactions between plants and insect herbivores. However, the effects of vehicle pollution on plant-insect interactions are poorly understood. Here, we combine a city-wide experiment across the Sacramento Metropolitan Area and a laboratory experiment to determine how vehicle emissions affect insect herbivory and leaf nutritional quality. We demonstrate that leaf damage to a native oak species (Quercus lobata) commonly planted across the western US is substantially elevated on trees exposed to vehicle emissions. In the laboratory, caterpillars preferred leaves from highway-adjacent trees and performed better on leaves from those same trees. Synthesis and applications. Together, our studies demonstrate that the heterogeneity in vehicle emissions across cities may explain highly variable patterns of insect herbivory on street trees. Our results also indicate that trees next to highways are particularly vulnerable to multiple stressors, including insect damage. To combat these effects, urban foresters may consider planting trees that are less susceptible to insect herbivory along heavily travelled roadways.

Video 1This video is our description of use of the novel dynamic rigidizing overtube in a challenging colonoscopy case in which looping was causing significant bradycardia and inability to advance the colonoscope to the cecum. The rigidizing overtube, through sequential cycles of its flexible and rigidized state, allowed for easy movement of the colonoscope to the cecum.

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Candida albicans invades endothelial cells by binding to N-cadherin and other cell surface receptors. This binding induces rearrangement of endothelial cell actin microfilaments, which results in the formation of pseudopods that surround the organism and pull it into the endothelial cell. Here, we investigated the role of endothelial cell septin 7 (SEPT7) in the endocytosis of C. albicans hyphae. Using confocal microscopy, we determined that SEPT7 accumulated with N-cadherin and actin microfilaments around C. albicans as it was endocytosed by endothelial cells. Affinity purification studies indicated that a complex containing N-cadherin and SEPT7 was recruited by C. albicans and that formation of this complex around C. albicans was mediated by the fungal Als3 and Ssa1 invasins. Knockdown of N-cadherin by small interfering RNA (siRNA) reduced recruitment of SEPT7 to C. albicans, suggesting that N-cadherin functions as a link between SEPT7 and the fungus. Also, depolymerization of actin microfilaments with cytochalasin D decreased the association between SEPT7 and N-cadherin and inhibited recruitment of both SEPT7 and N-cadherin to C. albicans, indicating the necessity of an intact cytoskeleton in the functional interaction between SEPT7 and N-cadherin. Importantly, knockdown of SEPT7 decreased accumulation of N-cadherin around C. albicans in intact endothelial cells and reduced binding of N-cadherin to this organism, as revealed by the affinity purification assay. Furthermore, SEPT7 knockdown significantly inhibited the endocytosis of C. albicans. Therefore, in response to C. albicans infection, SEPT7 forms a complex with endothelial cell N-cadherin, is required for normal accumulation of N-cadherin around C. albicans hyphae, and is necessary for maximal endocytosis of the organism.

Importance

During hematogenously disseminated infection, Candida albicans invades the endothelial cell lining of the blood vessels to invade the deep tissues. C. albicans can invade endothelial cells by inducing its own endocytosis, which is triggered when the C. albicans Als3 and Ssa1 invasins bind to N-cadherin on the endothelial cell surface. How this binding induces endocytosis is incompletely understood. Septins are intracellular GTP-binding proteins that influence the function and localization of cell surface proteins. We found that C. albicans Als3 and Ssa1 bind to a complex containing N-cadherin and septin 7, which in turn interacts with endothelial cell microfilaments, thereby inducing endocytosis of the organism. The key role of septin 7 in governing receptor-mediated endocytosis is likely relevant to host cell invasion by other microbial pathogens, in addition to C. albicans.

Coronary artery disease (CAD), the most common manifestation of cardiovascular disease, remains the most common cause of mortality in the United States. Risk assessment is key for primary prevention of coronary events and coronary artery calcium (CAC) scoring using computed tomography (CT) is one such non-invasive tool. Despite the proven clinical value of CAC, the current clinical practice implementation for CAC has limitations such as the lack of insurance coverage for the test, need for capital-intensive CT machines, specialized imaging protocols, and accredited 3D imaging labs for analysis (including personnel and software). Perhaps the greatest gap is the millions of patients who undergo routine chest CT exams and demonstrate coronary artery calcification, but their presence is not often reported or quantitation is not feasible. We present two deep learning models that automate CAC scoring demonstrating advantages in automated scoring for both dedicated gated coronary CT exams and routine non-gated chest CTs performed for other reasons to allow opportunistic screening. First, we trained a gated coronary CT model for CAC scoring that showed near perfect agreement (mean difference in scores = -2.86; Cohen's Kappa = 0.89, P < 0.0001) with current conventional manual scoring on a retrospective dataset of 79 patients and was found to perform the task faster (average time for automated CAC scoring using a graphics processing unit (GPU) was 3.5 ± 2.1 s vs. 261 s for manual scoring) in a prospective trial of 55 patients with little difference in scores compared to three technologists (mean difference in scores = 3.24, 5.12, and 5.48, respectively). Then using CAC scores from paired gated coronary CT as a reference standard, we trained a deep learning model on our internal data and a cohort from the Multi-Ethnic Study of Atherosclerosis (MESA) study (total training n = 341, Stanford test n = 42, MESA test n = 46) to perform CAC scoring on routine non-gated chest CT exams with validation on external datasets (total n = 303) obtained from four geographically disparate health systems. On identifying patients with any CAC (i.e., CAC ≥ 1), sensitivity and PPV was high across all datasets (ranges: 80-100% and 87-100%, respectively). For CAC ≥ 100 on routine non-gated chest CTs, which is the latest recommended threshold to initiate statin therapy, our model showed sensitivities of 71-94% and positive predictive values in the range of 88-100% across all the sites. Adoption of this model could allow more patients to be screened with CAC scoring, potentially allowing opportunistic early preventive interventions.